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Application of Machine Learning in Intelligent Medical Image Diagnosis and Construction of Intelligent Service Process

The introduction of digital technology in the healthcare industry is marked by ongoing difficulties with implementation and use. Slow progress has been made in unifying different healthcare systems, and much of the globe still lacks a fully integrated healthcare system. As a result, it is critical a...

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Autores principales: Gao, Zhihong, Lou, Lihua, Wang, Meihao, Sun, Zhen, Chen, Xiaodong, Zhang, Xiang, Pan, Zhifang, Hao, Haibin, Zhang, Yu, Quan, Shichao, Yin, Shaobo, Lin, Cai, Shen, Xian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9812610/
https://www.ncbi.nlm.nih.gov/pubmed/36619816
http://dx.doi.org/10.1155/2022/9152605
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author Gao, Zhihong
Lou, Lihua
Wang, Meihao
Sun, Zhen
Chen, Xiaodong
Zhang, Xiang
Pan, Zhifang
Hao, Haibin
Zhang, Yu
Quan, Shichao
Yin, Shaobo
Lin, Cai
Shen, Xian
author_facet Gao, Zhihong
Lou, Lihua
Wang, Meihao
Sun, Zhen
Chen, Xiaodong
Zhang, Xiang
Pan, Zhifang
Hao, Haibin
Zhang, Yu
Quan, Shichao
Yin, Shaobo
Lin, Cai
Shen, Xian
author_sort Gao, Zhihong
collection PubMed
description The introduction of digital technology in the healthcare industry is marked by ongoing difficulties with implementation and use. Slow progress has been made in unifying different healthcare systems, and much of the globe still lacks a fully integrated healthcare system. As a result, it is critical and advantageous for healthcare providers to comprehend the fundamental ideas of AI in order to design and deliver their own AI-powered technology. AI is commonly defined as the capacity of machines to mimic human cognitive functions. It can tackle jobs with equivalent or superior performance to humans by combining computer science, algorithms, machine learning, and data science. The healthcare system is a dynamic and evolving environment, and medical experts are constantly confronted with new issues, shifting duties, and frequent interruptions. Because of this variation, illness diagnosis frequently becomes a secondary concern for healthcare professionals. Furthermore, clinical interpretation of medical information is a cognitively demanding endeavor. This applies not just to seasoned experts, but also to individuals with varying or limited skills, such as young assistant doctors. In this paper, we proposed the comparative analysis of various state-of-the-art methods of deep learning for medical imaging diagnosis and evaluated various important characteristics. The methodology is to evaluate various important factors such as interpretability, visualization, semantic data, and quantification of logical relationships in medical data. Furthermore, the glaucoma diagnosis system is discussed in detail via qualitative and quantitative approaches. Finally, the applications and future prospects were also discussed.
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spelling pubmed-98126102023-01-05 Application of Machine Learning in Intelligent Medical Image Diagnosis and Construction of Intelligent Service Process Gao, Zhihong Lou, Lihua Wang, Meihao Sun, Zhen Chen, Xiaodong Zhang, Xiang Pan, Zhifang Hao, Haibin Zhang, Yu Quan, Shichao Yin, Shaobo Lin, Cai Shen, Xian Comput Intell Neurosci Review Article The introduction of digital technology in the healthcare industry is marked by ongoing difficulties with implementation and use. Slow progress has been made in unifying different healthcare systems, and much of the globe still lacks a fully integrated healthcare system. As a result, it is critical and advantageous for healthcare providers to comprehend the fundamental ideas of AI in order to design and deliver their own AI-powered technology. AI is commonly defined as the capacity of machines to mimic human cognitive functions. It can tackle jobs with equivalent or superior performance to humans by combining computer science, algorithms, machine learning, and data science. The healthcare system is a dynamic and evolving environment, and medical experts are constantly confronted with new issues, shifting duties, and frequent interruptions. Because of this variation, illness diagnosis frequently becomes a secondary concern for healthcare professionals. Furthermore, clinical interpretation of medical information is a cognitively demanding endeavor. This applies not just to seasoned experts, but also to individuals with varying or limited skills, such as young assistant doctors. In this paper, we proposed the comparative analysis of various state-of-the-art methods of deep learning for medical imaging diagnosis and evaluated various important characteristics. The methodology is to evaluate various important factors such as interpretability, visualization, semantic data, and quantification of logical relationships in medical data. Furthermore, the glaucoma diagnosis system is discussed in detail via qualitative and quantitative approaches. Finally, the applications and future prospects were also discussed. Hindawi 2022-12-28 /pmc/articles/PMC9812610/ /pubmed/36619816 http://dx.doi.org/10.1155/2022/9152605 Text en Copyright © 2022 Zhihong Gao et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Review Article
Gao, Zhihong
Lou, Lihua
Wang, Meihao
Sun, Zhen
Chen, Xiaodong
Zhang, Xiang
Pan, Zhifang
Hao, Haibin
Zhang, Yu
Quan, Shichao
Yin, Shaobo
Lin, Cai
Shen, Xian
Application of Machine Learning in Intelligent Medical Image Diagnosis and Construction of Intelligent Service Process
title Application of Machine Learning in Intelligent Medical Image Diagnosis and Construction of Intelligent Service Process
title_full Application of Machine Learning in Intelligent Medical Image Diagnosis and Construction of Intelligent Service Process
title_fullStr Application of Machine Learning in Intelligent Medical Image Diagnosis and Construction of Intelligent Service Process
title_full_unstemmed Application of Machine Learning in Intelligent Medical Image Diagnosis and Construction of Intelligent Service Process
title_short Application of Machine Learning in Intelligent Medical Image Diagnosis and Construction of Intelligent Service Process
title_sort application of machine learning in intelligent medical image diagnosis and construction of intelligent service process
topic Review Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9812610/
https://www.ncbi.nlm.nih.gov/pubmed/36619816
http://dx.doi.org/10.1155/2022/9152605
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